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GuidesBy professionData analysts, BI analysts and reporting teams

AI for data analysts who need the numbers to hold up

For Data analysts, BI analysts and reporting teams.

Use AI to move faster from a vague request to tested analysis, without handing over the definitions, checks or recommendation that make the result trustworthy.

When to use this

A stakeholder wants a dashboard or an answer by Friday. The data sits across several systems, the KPI is not fully defined, and everyone expects one clean result.

  • Turns the request into a sharper analysis brief, including the decision, audience and missing definitions
  • Drafts SQL, DAX, Python or spreadsheet logic and suggests checks for duplicates, missing values and broken joins
  • Challenges the first explanation, finds segments worth testing and helps turn verified findings into a clear business update

Workflow

  1. Rewrite the request as a decision, audience, metric and deadline

  2. List the trusted sources and definitions before asking AI for code

  3. Use AI to draft the analysis and validation checks, then run them in your approved environment

  4. Reconcile totals and test sample records before interpreting the result

  5. Ask for competing explanations, then publish only what the evidence supports

Prompts to try

Use it, adapt it, and make it your own.

Help me turn this request into a trustworthy analysis plan. The request is [request], the decision it should support is [decision], and the approved sources are [sources]. First identify missing definitions and likely data risks. Then propose the analysis steps, validation checks and competing explanations to test. Do not write a conclusion until I provide verified results.
Ask Bit to adapt it →

Human lens

  • You decide what the question really means and which source is trusted
  • You verify the grain, joins, KPI definition and every number that will be published
  • You judge whether the pattern matters in the real business and own the recommendation

What to avoid

  • Code that runs can still duplicate rows, use the wrong grain or apply the wrong business definition
  • A polished chart can hide missing segments, weak evidence or correlation presented as cause
  • Do not paste customer, employee or commercially sensitive data into a tool your organisation has not approved

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Tools that fit: ChatGPT, Claude, Gemini, Kimi · Reviewed 2026-07-28